Hybrid multiobjective artificial bee colony for multiple sequence alignment

Graphical abstractDisplay Omitted HighlightsWe propose multiobjective evolutionary computation for multiple sequence alignment.We optimize two widely-used objective functions in the literature: WSP and TC.Hybridization of ABC and Kalign2 to obtain more accurate alignments.A study between our proposal and 13 aligners from the bioinformatics field. In the bioinformatics community, it is really important to find an accurate and simultaneous alignment among diverse biological sequences which are assumed to have an evolutionary relationship. From the alignment, the sequences homology is inferred and the shared evolutionary origins among the sequences are extracted by using phylogenetic analysis. This problem is known as the multiple sequence alignment (MSA) problem. In the literature, several approaches have been proposed to solve the MSA problem, such as progressive alignments methods, consistency-based algorithms, or genetic algorithms (GAs). In this work, we propose a Hybrid Multiobjective Evolutionary Algorithm based on the behaviour of honey bees for solving the MSA problem, the hybrid multiobjective artificial bee colony (HMOABC) algorithm. HMOABC considers two objective functions with the aim of preserving the quality and consistency of the alignment: the weighted sum-of-pairs function with affine gap penalties (WSP) and the number of totally conserved (TC) columns score. In order to assess the accuracy of HMOABC, we have used the BAliBASE benchmark (version 3.0), which according to the developers presents more challenging test cases representing the real problems encountered when aligning large sets of complex sequences. Our multiobjective approach has been compared with 13 well-known methods in bioinformatics field and with other 6 evolutionary algorithms published in the literature.

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